Music recommendations from trending queries

ABSTRACT

A plurality of entities relating to popular search queries are identified. A set of entities representing musical artists or events is selected from the plurality of entities. Based on a history of online actions of a user, a subset of the selected set of entities that is relevant to the user is determined, and personalized music recommendations are created for the user, where the personalized music recommendations comprise music content associated with the determined subset of entities that each represent a musical artist or event relating to the popular search queries. The personalized music recommendations are provided for presentation to the user.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/440,312, filed Dec. 29, 2016, entitled “Music Recommendations FromTrending Queries,” which is incorporated by reference herein.

TECHNICAL FIELD

This disclosure relates to the field of music recommendations and, inparticular, to personalized music recommendations on a content sharingplatform.

BACKGROUND

On the Internet, social media platforms (e.g., social network platforms,content sharing platforms, etc.) allow users to connect to and shareinformation with each other. Many social media platforms include acontent sharing aspect that allows users to upload, view, and sharecontent, such as video content, image content, audio content, textcontent, and so on (which may be collectively referred to as “mediaitems” or “content items”). Such media items may include audio clips,movie clips, TV clips, and music videos, as well as amateur content suchas video blogging, short original videos, pictures, photos, othermultimedia content, etc. Users may use computing devices (e.g., clientdevices such as smart phones, cellular phones, laptop computers, desktopcomputers, netbooks, tablet computers) to play and/or consume mediaitems (e.g., watch digital videos, and/or listen to digital music).

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate any scope of the particular implementations of the disclosureor any scope of the claims. Its sole purpose is to present some conceptsof the disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

In an aspect of the disclosure, a method for providing personalizedmusic recommendations to a user includes: identifying, by a processingdevice of a content sharing platform, a plurality of entities relatingto popular search queries (e.g., search queries popular currently, on agiven day in the past, over a recent period of time, etc.); selecting,from the plurality of entities, a set of entities representing trendingentities such as musical artists or events; determining, based on ahistory of online actions of the user, a subset of the selected set ofentities that is relevant to the user; creating, by the processingdevice, the personalized music recommendations for the user, thepersonalized music recommendations comprising music content associatedwith the determined subset of entities that each represent a musicalartist or an event relating to the currently popular search queries; andproviding the personalized music recommendations for presentation to theuser.

In some implementations, the popular search queries are search queriessubmitted by at least a threshold number of users via one or moreexternal search engine platforms over a predefined time period.

In some implementations, the musical artists comprise one or more of asinger, a musician, a composer, a music video director, a music videoproducer, or a band.

In some implementations, the online actions of the user comprise one ormore of submitting a search query, accessing a media item, or consuminga media item, wherein consuming the media item comprises watching orlistening to the media item.

In some implementations, determining, based on the history of onlineactions of the user, the subset of the selected set of entitiescomprises: identifying entities such as musical artists or eventsassociated with the online actions of the user; and determining whetherany of the identified entities matches any of the selected set ofentities relating to the currently popular search queries.

In some implementations, creating the personalized music recommendationsfor the user comprises: assigning a score to each of the determinedsubset of entities based on popularity of respective search queries andrelevancy to the user.

In some implementations, creating the personalized music recommendationsfor the user further comprises: identifying music recommendationcandidates that are ranked based on a plurality of factors; andimproving a ranking of any of the identified music recommendationcandidates that matches one of the determined subset of entities basedon a score assigned to a respective entity of the determined subset ofentities.

In some implementations, the method further includes: identifying areference to an artist profile associated with a selected entity (e.g.,a selected musical artist or a selected event) in the determinedselected subset of entities; and providing the reference forpresentation to the user together with the personalized musicrecommendations.

In some implementations, the method further includes: identifying anonline document indicating why a selected entity (e.g., a selectedmusical artist or a selected event) in the determined selected subset ofentities is currently popular; and providing a reference to the onlinedocument for presentation to the user together with the personalizedmusic recommendations

In some implementations, the music content of the personalized musicrecommendations is at least one of a playlist or a channel.

In another aspect of the disclosure, a method for providing personalizedmusic recommendations to a user includes: identifying, by a processingdevice of a content sharing platform, a plurality of music playlistscreated on the content sharing platform, each of the plurality of musicplaylists having a ranking; identifying a plurality of popular externalsearch queries submitted via one or more search engine platformsexternal to the content sharing platform; determining a subset of theplurality of music playlists that matches any of the plurality ofcurrently popular external search queries; improving rankings of thesubset of the music playlists based on the plurality of popular externalsearch queries; creating, by the processing device, the personalizedmusic recommendations for the user based on rankings of the plurality ofmusic playlists; and providing the personalized music recommendation forpresentation to the user.

In some implementations, the plurality of the popular external searchqueries are submitted by at least a threshold number of users over apredefined time period.

In some implementations, the method further includes: determining aplurality of musical artists that are each associated with one of theplurality of music playlists; determining, based on a history of onlineactions of the user, one or more of the plurality of music playliststhat are relevant to the user; and improving rankings of the determinedone or more music playlists.

In some implementations, the plurality of musical artists comprises oneor more of a singer, a musician, a composer, a music video director, alyrics creator, a music video producer, or a music band.

In some implementations, the online actions of the user comprise one ormore of submitting a search query, accessing a media item, or consuminga media item, wherein consuming the media item comprises watching orlistening to the media item.

In some implementations, the method further includes: identifying anonline document indicating why a respective external search query iscurrently popular; and providing a reference to the online document forpresentation to the user together with the personalized musicrecommendations.

Further, computing devices for performing the operations of the abovedescribed methods and the various implementations described herein aredisclosed. Computer-readable media that store instructions forperforming operations associated with the above described methods andthe various implementations described herein are also disclosed.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating an example network architecturein which aspects of the present disclosure may be implemented.

FIG. 2 is a block diagram illustrating an example music recommendationsystem in accordance with some aspects of the present disclosure.

FIG. 3 is a flow diagram illustrating a method for providingpersonalized music recommendations to a user based on trending entities,according to an implementation.

FIG. 4 is a flow diagram illustrating a method for providing musicrecommendations to a user based on current events, according to animplementation.

FIG. 5 illustrates an example personalized music recommendationgraphical user interface in accordance with some aspects of thedisclosure.

FIG. 6 is a block diagram illustrating an example computer system inwhich aspects of the disclosure can be implemented.

DETAILED DESCRIPTION

Aspects of the disclosure are directed to providing personalized musicrecommendations on a content sharing platform. A content sharingplatform allows users to find and consume (watch and/or listen to)various media items including, for example, music content items such asmusic videos and songs.

One of the ways in which content sharing platforms retain users is byproviding high quality media item (e.g., music) recommendations. Userstend to listen to artists they like and to music that is relevant totheir current contextual surroundings. In conventional systems, musicrecommendations are usually created based on popular music media itemsoffered on the content sharing platform or based on similarities betweena song a user is known to like, and other songs that share similarcharacteristics. However, a user's music preference may change on adaily basis, based on his or her contextual surroundings (e.g., currentevents, trending topics, political atmosphere, etc.). Conventionalsystem typically do not consider contextual surroundings of a user whencreating recommendations for the user, resulting in recommended contentthat may not fully reflect the user’ needs.

Aspects of the present disclosure address the above and otherdeficiencies by providing personalized music recommendations thatreflect current contextual surroundings of users of a content sharingplatform. In some implementations, a music recommendation system of thecontent sharing platform provides to a user music recommendations thatmatch both the user's known taste in music (e.g., based on a history ofmusic consumption of the user) and currently “trending” entities such astrending musical artists. A trending musical artist is a musical artistrelating to (e.g., referenced in or contextually associated with)popular search queries (e.g., search queries popular currently, on agiven day in the past, over a recent period of time, etc.). A musicalartist can be a singer, a composer, a musician, a band, a music videoproducer, a music video director, a lyrics creator, etc. In one example,death of famous singer X may result in a large number of search queriespertaining to singer X. The music recommendation system may determine,based on the currently popular search queries, that singer X is atrending musical artist, and that singer X is also one of musicalartists included in the viewing/listening history of user A. Based onthis determination, the music recommendation system may recommend one ormore music playlists (also referred to as steaming radio stations orradios) focused on different songs of singer X to user A.

In other implementations, the music recommendation system may recommendmusic playlists created on the content sharing platform if such musicplaylists have a correlation with current events as reflected in popular(“trending”) external search queries submitted by various users viaexternal search engine platforms. For example, the content sharingplatform may create music playlists for various users and store them forfuture use. When selecting recommendations, the music recommendationsystem may identify popular (trending) external search queries, and maydetermine which of the created music playlists match any of the trendingexternal search queries to find music playlists that are relevant tocurrent events (as reflected by the trending external search queries).The music recommendation system may further recommend at least some ofthe found music playlists to a user.

According to some aspects of the disclosure, the music recommendationsystem may also provide an explanation of why the music playlists arebeing recommended to the user. For example, the music recommendationsystem may specify that a music playlist is recommended because it isassociated with a trending musical artist or a trending search query,and may indicate why such a musical artist or search query is trending(e.g., due to release of the musical artist's album, due death of themusical artists, due to an upcoming holiday such as Valentine's Day,etc.). As such, the user is provided some context as to why therecommendation is being made.

By selecting music content based on trending musical artists and currentevents, the technology disclosed herein advantageously providesrecommendations that reflect current contextual surroundings of a user.This results in recommendations that are more relevant and moreinteresting to the user, thereby improving an overall user experiencewith the content sharing platform, and increasing the number of musicvideos and songs consumed by the user. In addition, the use of searchqueries to identify trending musical artists and current events allowsfor more accurate and more efficient identification of trends than theconventional approach that uses popularity of music videos and songs onthe content sharing platform because the volume of search queries ishigher (and as such provides for more accurate identification of trends)and the use of search queries involves fewer operations (and as suchrequires less computing resources) than the use of popular musicalvideos and songs. In particular, the operations pertaining to the use ofsearch queries can involve, for example, (i) identifying entitiesmentioned in search queries, and (ii) aggregating search queries basedon the identified entities. In contrast, the operations pertaining tothe use of popular music videos and songs typically involve (i)collecting statistics on consumption history (viewing, listening orapproving) of music videos and songs, (ii) identifying popular musicvideos and songs based on the collected statistics, (iii) examiningmetadata and/or descriptions of the popular music videos and songs toextract associated entities, and (iv) aggregating the popular musicalvideos and songs based on the extracted associated entities.Furthermore, by providing recommendations that users are more likely tofollow, the technology disclosed herein results in more effective use ofprocessing and storage resources.

The present disclosure often references music media items for simplicityand brevity. However, the teaching of the present disclosure can beapplied to various other types of content or media items, including, forexample, movies, audio books and images among others.

FIG. 1 illustrates an example system architecture 100 that includesclient devices 110 a through 110 z, a network 105, a data store 106, acontent sharing platform 120, a server 130, and third-party platform(s)150. In one implementation, network 105 may include a public network(e.g., the Internet), a private network (e.g., a local area network(LAN) or wide area network (WAN)), a wired network (e.g., Ethernetnetwork), a wireless network (e.g., an 802.11 network or a Wi-Finetwork), a cellular network (e.g., a Long Term Evolution (LTE)network), routers, hubs, switches, server computers, and/or acombination thereof. In one implementation, the data store 106 may be amemory (e.g., random access memory), a cache, a drive (e.g., a harddrive), a flash drive, a database system, or another type of componentor device capable of storing data. The data store 106 may also includemultiple storage components (e.g., multiple drives or multipledatabases) that may also span multiple computing devices (e.g., multipleserver computers).

The client devices 110 a through 110 z may each include computingdevices such as personal computers (PCs), laptops, mobile phones, smartphones, tablet computers, network connected televisions, netbookcomputers etc. In some implementations, client devices 110 a through 110z may also be referred to as “user devices.” Each client device includesa respective media viewer 112 a-z. In one implementation, the mediaviewers 112 a-z may be applications that allow users to view and/orlisten to media content, such as images, videos, songs (e.g., musiccontent items), web pages, documents, etc. For example, the mediaviewers 112 a-z may be a web browser that can access, retrieve, present,and/or navigate content (e.g., web pages such as Hyper Text MarkupLanguage (HTML) pages, digital media items or content items, etc.)served by a web server. The media viewers 112 a-z may render, display,and/or present the content (e.g., web pages) to a user. The mediaviewers 112 a-z may also display an embedded media player (e.g., aFlash® player or an HTML5 player) that is embedded in a web page (e.g.,a web page that may provide information about a product sold by anonline merchant). In another example, the media viewers 112 a-z may bestandalone applications (e.g., mobile applications) that allow users toview and/or listen to digital media content items (e.g., digital videos,songs, digital images, electronic books, etc.).

The media viewers 112 a-z may be provided to the client devices 110 athrough 110 z by the server 130 and/or content sharing platform 120. Forexample, the media viewers 112 a-z may be embedded media players thatare embedded in web pages provided by the content sharing platform 120.In another example, the media viewers 112 a-z may be applications thatcommunicate with the server 130 and/or content service provider 120.

It should be noted that functions described in one implementation asbeing performed by the content sharing platform 120 can also beperformed on the client devices 110 a through 110 z, and/or server 130in other implementations, if appropriate. In addition, the functionalityattributed to a particular component can be performed by different ormultiple components operating together. The content sharing platform 120can also be accessed as a service provided to other systems or devicesthrough appropriate application programming interfaces, and thus is notlimited to use in websites.

In one implementation, the content sharing platform 120 may be one ormore computing devices (such as a rackmount server, a router computer, aserver computer, a personal computer, a mainframe computer, a laptopcomputer, a tablet computer, a network connected television, a desktopcomputer, etc.), data stores (e.g., hard disks, memories, databases),networks, software components, and/or hardware components that may beused to provide a user with access to media items (also referred to ascontent items) and/or provide the media items to the user. For example,the content sharing platform 120 may allow a user to consume, upload,search for, approve of (“like”), dislike, and/or comment on media items.The content sharing platform 120 may also include a website (e.g., awebpage) that may be used to provide a user with access to the mediaitems.

In implementations of the disclosure, a “user,” a “content creator,” ora “channel owner” may be represented as a single individual. However,other implementations of the disclosure encompass a “user,” a “contentcreator,” or a “channel owner” being an entity controlled by a set ofusers and/or an automated source. For example, a set of individual usersfederated as a community in a social network may be considered a “user,”a “content creator,” or a “channel owner.” In another example, anautomated consumer may be an automated ingestion pipeline, such as atopic channel, of the content sharing platform 120.

The content sharing platform 120 may include multiple channels (e.g.,channels A through Z). A channel can be data content available from acommon source or data content having a common topic, theme, orsubstance. The data content can be digital content chosen by a user,digital content made available by a user, digital content uploaded by auser, digital content chosen by a content provider, digital contentchosen by a broadcaster, etc. A channel may include one or more mediaitems 121 a-n or 122 a-n (e.g., music content items). A channel can beassociated with an owner, who is a user that can perform actions on thechannel. Different activities can be associated with the channel basedon the owner's actions, such as the owner making digital contentavailable on the channel, the owner selecting (e.g., liking) digitalcontent associated with another channel, the owner commenting on digitalcontent associated with another channel, etc. The activities associatedwith the channel can be collected into an activity feed for the channel.Users, other than the owner of the channel, can subscribe to one or morechannels in which they are interested. The concept of “subscribing” mayalso be referred to as “liking,” “following,” “friending,” and so on.

Once a user subscribes to a channel, the user can be presented withinformation from the channel's activity feed. If a user subscribes tomultiple channels, the activity feed for each channel to which the useris subscribed can be combined into a syndicated activity feed.Information from the syndicated activity feed can be presented to theuser. Channels may have their own feeds. For example, when navigating toa home page of a channel on the content sharing platform, feed itemsproduced by that channel may be shown on the channel home page. Usersmay have a syndicated feed, which is a feed comprised of at least asubset of the content items from all of the channels to which the useris subscribed. Syndicated feeds may also include content items fromchannels to which the user is not subscribed. For example, the contentsharing platform 120 or other social networks may insert recommendedcontent items into the user's syndicated feed, or may insert contentitems associated with a related connection of the user in the syndicatedfeed.

The content sharing platform 120 may also include multiple playlists127. A playlist represents a collection of media items that areconfigured to play one after another in a particular order without anyuser interaction. When the last media item stops playing, the firstmedia item may start playing again, providing a continuous viewing orlistening experience for the user. A streaming radio station alsoreferred to as a radio may correspond to a playlist that provides adynamic, continuous stream of audio that may or may not be paused orreplayed (similarly to a traditional broadcast media radio station). Insome implementations, streaming radio stations may be buffered foroffline access. As used herein, the term “music playlist” covers astreaming radio station providing audio music content, as well as aplaylist providing music videos and/or other type of content.

Playlists may be curated manually or automatically (e.g., according togenre, artist, band, tempo, dates, etc.). Media items included inplaylist 127 may be from the same channel, different channels orindependent media items that are not part of any channels. Examples ofmedia items 121 a-n and 122 a-n can include, and are not limited to,digital videos, digital movies, digital photos, digital music, websitecontent, social media updates, electronic books (ebooks), electronicmagazines, digital newspapers, digital audio books, electronic journals,web blogs, real simple syndication (RSS) feeds, electronic comic books,software applications, etc. Media items 121 a-n and 122 a-n, alsoreferred to herein as music content items, music videos and songs, caninclude an electronic file that can be executed or loaded usingsoftware, firmware or hardware configured to present the digital mediaitem to an entity. In one implementation, the content sharing platform120 may store the media items 121 a-n and 122 a-n using the data store106.

The content sharing platform 120 may include content consumption system125 that determines a history of user actions associated with mediaitems content 121 a-n, 122 a-n. In one example, the content consumptionsystem 125 may store a list of music content items accessed, watched orlistened to by a user. In another example, the content consumptionsystem 125 may also store search queries submitted by a user on thecontent sharing platform 120.

The content sharing platform 120 may be associated with or include amusic recommendation system 140 hosted by the server 130. The server 130may be one or more computing devices (e.g., a rackmount server, a servercomputer, etc.). The music recommendation system 140 may providepersonalized music recommendations to users of the content sharingplatform 120. As discussed in more detail herein, these musicrecommendations may reflect contextual surroundings of the user such ascurrent events and trending musical artists and may be provided via userinterfaces (UIs) of the content sharing platform 120, including a homechannel page UI, a search UI, a playlist UI, etc., and may be presentedas feed items or in any other form.

Users of the content sharing platform 120 may also interact withthird-party platform(s) 150. A third-party platform 150 may be a socialnetwork platform (e.g., social media platform), a search engineplatform, another content sharing platform, etc. Another type ofthird-party platform may be a query statistics service that determineshow often a particular search-term is entered via one or more searchengine platforms relative to the total search-volume on the one or moresearch engine platforms across various regions of the world, and invarious languages. Based on these determinations, the query statisticsservice may identify trending search queries and may provide thesetrending search queries to the music recommendation system 140.

Although implementations of the disclosure are discussed in terms ofcontent sharing platforms and providing personalized musicrecommendations on the content sharing platform, implementations mayalso be generally applied to any type of social media platform providingmedia item recommendations. Such social media platforms are not limitedto content sharing platforms that provide channel subscriptions,playlists, and/or internet radio stations to users.

In situations in which the systems discussed herein collect personalinformation about users, or may make use of personal information, theusers may be provided with an opportunity to control whether the contentsharing platform 120 collects user information (e.g., information abouta user's social network, social actions or activities, profession, auser's preferences, a user's purchase transaction history, or a user'scurrent location), or to control whether and/or how to receive contentfrom the content server that may be more relevant to the user. Inaddition, certain data may be treated in one or more ways before it isstored or used, so that personally identifiable information is removed.For example, a user's identity may be treated so that no personallyidentifiable information can be determined for the user, or a user'sgeographic location may be generalized where location information isobtained (such as to a city, ZIP code, or state level), so that aparticular location of a user cannot be determined. Thus, the user mayhave control over how information is collected about the user and usedby the content sharing platform 120.

FIG. 2 is a block diagram illustrating an example music recommendationsystem 140 in accordance with some aspects of the present disclosure. Inone implementation, music recommendation system 140 includes userhistory analyzer 202, trending musical artist selector 204,recommendation creator 206, ranking generator 208, trending queryplaylist selector 210, and artist data store 216. Music recommendationsystem 140 may communicate with third-party platform (e.g., querystatistics service) 150 and/or content consumption system 125.

The artist data store 216 may be part of or external to musicrecommendation system 140 and may reside in a memory (e.g., randomaccess memory), a cache, a drive (e.g., a hard drive), a flash drive, adatabase system, or another type of component or device capable ofstoring data. The artist data store 216 may also include multiplestorage components (e.g., multiple drives or multiple databases) thatmay also span multiple computing devices (e.g., multiple servercomputers). Artist data store 216 may store information about musicalartists associated with media items 121 a-n and 122 a-n of contentsharing platform 120. The information about musical artists may becompiled as music media items are uploaded to the content sharingplatform 120 (e.g., based on metadata provided as part of or in additionto the music media items).

The trending musical artist selector 204 may identify entities relatingto popular (trending) search queries. The trending search queries may besearch queries submitted by at least a threshold number of users via oneor more external search engine platforms over a predefined time periodfrom the present time or on a given day or time period in the past(e.g., a specific day of a previous week or the entire previous week).The trending search queries may be identified by the query statisticsservice that groups various queries directed to similar topics,subjects, etc. and provides clusters of trending (frequently used) queryterms. The trending musical artist selector 204 may determine (e.g.,using a knowledge base tool analyzing semantic search information) anentity relating to a cluster of trending queries (or query terms). Thisentity may be a person or an organization. The trending musical artistselector 204 may select, from the trending entities, those entities thatrepresent musical artists (e.g., singers, musicians, composers, musicvideo directors, music video producers, music bands, etc.) based oninformation about musical artists in the artist data store 216.

The user history analyzer 202 may review a history of online actions ofa user and determine, based on this history, which of the selectedentities are relevant to a specific user. For example, the user's onlineactions may include search queries submitted by the user on the contentsharing platform 120 and/or via one or more external search engineplatforms 150, the user's accesses of media items, the user'sconsumption (watching or listening) of media items, the user's approvalor disapproval of media items, the user's behavior while consuming mediaitems (e.g., pausing, skipping, fast-forwarding, rewinding, etc.), etc.The user history may be created using the content consumption system 125and/or a search engine system of the content sharing platform 120 andmay include musical artists relating to the above user actions. The userhistory may be in the form of the user's music profile (that specifiesthe user's favorite musical artists ranked based on the user's impliedor expressed interest in these musical artists) or any other form orstructure. The user history analyzer 202 may select musical artists fromthe user history that match any of the musical artists relating to thetrending search queries. A musical artist relating to a trending searchquery is referred to herein as a trending musical artist.

The recommendation creator 206 may find music content (e.g., musicplaylists, music channels, etc.) associated with the selected musicartists, create personalized music recommendations including the musiccontent, and provide the personalized music recommendations forpresentation to the user. The music content may refer to music playlists(including streaming radio stations), music channels, and/or individualmusic media items. Music playlists may be playlists previously createdautomatically or by other users or playlists that are being createdautomatically for selected trending musical artists that are alsoreflected in the user history.

In some implementations, the recommendation creator 206 may providerecommendations based on rankings provided by the ranking generator 208.For example, the ranking generator 208 may assign a score to eachselected musical artist that is both trending and indicated in the userhistory based on popularity (“trendiness”) of the selected musicalartist (e.g., a frequency of search queries related to the selectedmusical artist) and relevancy of the selected musical artist to the user(e.g., how frequently the user performs an online action with respect tomusical content related to the selected musical artist). The rankinggenerator 208 may then improve (e.g., boost) rankings of musicrecommendation candidates that match the selected musical artists basedon respective assigned scores. When creating recommendations, therecommendation creator 206 may choose music recommendation candidateswith the highest rankings (e.g., 20 playlists and/or channels withhighest rankings) and present them to the user in the order defined bythe rankings.

In some implementations, the music recommendation candidates may onlyinclude music content associated with the musical artists that are bothtrending and indicated in the user history. Alternatively, the musicrecommendation candidates may also include music media items that arepopular (frequently requested and/or consumed) on the content sharingplatform, music media items consumed by the user's contacts, music mediaitems that are similar to those consumed by the user, etc. All of thesemusic items may be ranked based on popularity and relevancy to the user,where relevancy to the user may be defined by the user history of onlineactions, and/or user demographics (e.g., geographic location, language,etc.). As discussed above, the ranking generator 208 may improve (e.g.,boost) rankings of music recommendation candidates related to musicalartists that are both trending and from the user's history. Therecommendation creator 206 may then use the resulting rankings of themusic recommendation candidates to select music media items with highestrankings for recommendation to the user. The music recommendations maybe provided via UIs of the content sharing platform 120, including ahome channel page UI, a search UI, a playlist UI, etc., and may bepresented as feed items or in any other form.

In one implementation, for recommended music content related to atrending musical artist, the recommendation creator 206 may include areference to an artist profile of the trending musical artist andprovide this reference for presentation to the user together with therespective music recommendation. The reference to the artist profile maydirect a user to a webpage on the content sharing platform or to awebpage located elsewhere on the Internet. The reference may be a link(e.g., HyperText Transport Protocol (HTTP) link, hyperlink, etc.)

Alternatively or in addition, recommendation creator 206 may identify anonline article or a similar online document indicating why the musicalartist is trending (e.g., due to release of the musical artist's album,death of the musical artists, etc.), and provide a reference (e.g.,link) to this online document for presentation to the user together withthe respective music recommendation. According to some implementations,recommendation creator 206 may also or alternatively provide a summary(e.g., 1-2 sentences) specifying why the recommendation is providedand/or an entity is trending (e.g., due to release of a musical artist'salbum, death of a musical artists, etc.). The summary may be createdbased on user input or generated automatically by searching onlinesources (e.g., predefined online news services) using the name of atrending musical artist and extracting information about an eventassociated with the trending musical artist. As such, the user isprovided some context as to why the recommendation was made.

In yet another implementation, the reference to the online document orthe summary may be saved in association with the respective musicplaylist, as well as the period of time during which the musical artistrelating to the music playlist has been trending, and this information(the period of time and/or the reference to the online document) may besubsequently presented on a playlist UI of the music playlist.

According to other aspects of the present implementations, the musicrecommendation system 140 may recommend music playlists created on thecontent sharing platform 120 if such music playlists have a correlationwith current events as reflected in currently trending external searchqueries submitted by various users via external search engine platforms.Current events may refer to holidays, new music and/or video releaseevents, political events, popular cultural events, trending social mediaevents, or any other single-day or multi-day events.

In some implementations, the trending query playlist selector 210 mayreceive currently trending external search queries (e.g., from the querystatistics service that groups various queries directed to similartopics, subjects, etc. and provides clusters of trending query terms)and may correlate these trending external search queries with musicplaylists created on and/or hosted by the content sharing platform. Thecorrelation may be done by comparing entities mentioned in the trendingexternal search queries (e.g., using clusters of trending query terms)with information pertaining to the created music playlists. Informationpertaining to a created music playlist may include, for example, thetitle and/or the description of the music playlist, titles and/ordescriptions of music videos or songs from the music playlist, or anycombination of the above. Upon finding a matching music playlist, thetrending query playlist selector 210 may add the matching music playlistto music recommendation candidates that are being created for the user.The ranking generator 208 may improve (e.g., boost) rankings of themusic playlists that match trending external search queries. In someimplementations, the user history analyzer 202 may use the user historyto further filter out the selected music playlists. In particular, theuser history analyzer 202 may determine musical artists relating to theselected music playlists, match the determined artists with musicalartists referenced in the user history, and further improve (e.g.,boost) rankings of music playlists related to the musical artists thatare also referenced in the user history.

When creating recommendations, the recommendation creator 206 may choosemusic recommendation candidates with the highest rankings (e.g., 20playlists and/or channels with highest rankings) and present them to theuser in the order defined by the rankings.

In some implementations, the music recommendation candidates may includemusic media items that are popular (frequently requested and/orconsumed) on the content sharing platform, music media items consumed bythe user's contacts, music media items that are similar to thoseconsumed by the user, etc. All of these music items may be ranked basedon popularity and relevancy to the user, where relevancy to the user maybe defined by the user history of online actions, and/or userdemographics (e.g., geographic location, language, etc.). Therecommendation creator 206 may use the resulting rankings of the musicrecommendation candidates to select music media items with highestrankings for recommendation to the user. The music recommendations maybe provided via UIs of the content sharing platform 120, including ahome channel page UI, a search UI, a playlist UI, etc., and may bepresented as feed items or in any other form.

In one implementation, recommendation creator 206 may identify an onlinearticle or a similar online document about a current event causing thesearch query that was used to create a respective music playlist to betrending (e.g., Valentine's Day, Museum Mile Festival in New York City,etc.), and provide a reference (e.g., link) to this online document forpresentation to the user together with the respective musicrecommendation. Alternatively or in addition, recommendation creator 206may create a summary (e.g., 1-2 sentences) specifying why therecommendation is being provided or an entity is trending (e.g., due torelease of a musical artist's album, death of a musical artists, anupcoming holiday, a political event, etc.). The summary may be createdbased on user input or generated automatically by searching onlinesources (e.g., predefined online news services) using the name of atrending musical artist and extracting information about an eventassociated with the trending musical artist. As such, the user isprovided some context as to why the recommendation was made. In yetanother implementation, the reference to the online document and/or thesummaries may be saved in association with the respective musicplaylist, as well as the period of time during which the search queryused to create the music playlist has been trending, and thisinformation (the period of time and/or the reference to the onlinedocument) may be subsequently presented on a playlist UI of the musicplaylist.

FIGS. 3-4 depict flow diagrams for illustrative examples of methods 300and 400 for providing personalized music recommendations to a user.Methods 300 and 400 may be performed by processing devices that mayinclude hardware (e.g., circuitry, dedicated logic), software (such asis run on a general purpose computer system or a dedicated machine), ora combination of both. Methods 300 and 400 and each of their individualfunctions, routines, subroutines, or operations may be performed by oneor more processors of the computer device executing the method. Incertain implementations, methods 300 and 400 may each be performed by asingle processing thread. Alternatively, methods 300 and 400 may beperformed by two or more processing threads, each thread executing oneor more individual functions, routines, subroutines, or operations ofthe method.

For simplicity of explanation, the methods of this disclosure aredepicted and described as a series of acts. However, acts in accordancewith this disclosure can occur in various orders and/or concurrently,and with other acts not presented and described herein. Furthermore, notall illustrated acts may be required to implement the methods inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the methods couldalternatively be represented as a series of interrelated states via astate diagram or events. Additionally, it should be appreciated that themethods disclosed in this specification are capable of being stored onan article of manufacture to facilitate transporting and transferringsuch methods to computing devices. The term “article of manufacture,” asused herein, is intended to encompass a computer program accessible fromany computer-readable device or storage media.

FIG. 3 is a flow diagram illustrating a method 300 for providingpersonalized music recommendations to a user based on trending entities,according to an implementation. Method 300 may be performed by contentsharing platform 120 and/or music recommendation system 140 of FIG. 1.

Referring to FIG. 3, at block 302, processing logic identifies trendingentities (e.g., entities relating to currently popular search queries).At block 304, processing logic selects, from the trending entities, aset of entities representing musical artists.

At block 306, processing logic determines, based on a history of onlineactions of the user, a subset of the selected set of entities that isrelevant to the user. As discussed above, this determination can be madeby correlating trending musical artists with musical artists referencedin the user history (e.g., user profile). In addition, the determinationmay be also based on characteristics of the user (e.g., user country ofresidence, user language, user cultural background, etc.).Advantageously, the determination performed at block 306 allows for theselection of currently trending musical artists who are likely to be ofinterest to the user. At block 308, processing logic creates thepersonalized music recommendations for the user. In one implementation,the personalized music recommendations include music content associatedwith the determined subset of entities that each represents a trendingmusical artist. At block 310, processing logic provides the personalizedmusic recommendations for presentation to the user.

FIG. 4 is a flow diagram illustrating a method 400 for providingpersonalized music recommendations to a user based on current events,according to an implementation. Method 400 may be performed by contentsharing platform 120 and/or music recommendation system 140 of FIG. 1.

Referring to FIG. 4, at block 402, processing logic identifies musicplaylists created on the content sharing platform. Each of the musicplaylist may have a ranking based on its popularity on the contentsharing platform, relevancy to the user (e.g., higher ranking if theplaylist was created for the user (e.g., based on consumption history orpreferences of the user) and lower ranking if the playlist was createdfor another user (e.g., based on consumption history or preferences ofanother user)).

At block 404, processing logic identifies trending external searchqueries submitted via one or more search engine platforms external tothe content sharing platform. At block 406, processing logic correlatesthe music playlists to the trending external search queries. At block408, processing logic identifies a subset of the music playlists thatmatch any of the trending external search queries, and improves rankingsof the identified music playlists.

In some implementations, processing logic may also use the user historyto further match musical artists relating to the music playlistsidentified at block 408 with musical artists referenced in the userhistory. When such a match is found, processing logic may furtherimprove the ranking of a respective music playlist.

At block 410, processing logic creates personalized musicrecommendations for the user based on resulting rankings of musicplaylists. At block 412, processing logic provides the personalizedmusic recommendations for presentation to the user.

FIG. 5 illustrates an example personalized music recommendationgraphical user interface (GUI) 500 in accordance with some aspects ofthe disclosure. GUI 500 may be provided by content sharing platform 120of FIG. 1.

GUI 500 may be displayed on a client device (e.g., client device 110) ofa user, and may include a media player portion 502 in which a mediaplayer presents and plays a music media item (e.g., obtained fromcontent sharing platform 120 of FIG. 1). GUI 500 may also include arecommendation portion 504 presenting (e.g., in a feed) personalizedrecommendations 504 based on trending musical artists.

Each personalized recommendation may be a music playlist (including astreaming radio station), a music channel or an individual music mediaitem. For example, a personalized recommendation may be a music playlistthat can be represented by a visual indicator (e.g., an image, name,etc.) 506 that may be selected by the user to start playing the musicplaylist and/or navigate to a separate GUI to view more informationabout the playlist. In one implementation, GUI 500 includes a link 508to an artist profile of the related musical artist, and/or a link 510 toan online document (e.g., news article) that provides an explanation ofwhy the related musical artist is trending, as described in more detailherein. Alternatively or in addition, a summary (e.g., 1-2 sentences)specifying why the recommendation is being provided (e.g., due torelease of a musical artist's album, death of a musical artists, etc.)may be provided, as described in more detail herein.

FIG. 6 illustrates a diagrammatic representation of a machine in theexemplary form of a computer system 600 within which a set ofinstructions, for causing the machine to perform any one or more of themethodologies discussed herein, may be executed. In alternativeimplementations, the machine may be connected (e.g., networked) to othermachines in a local area network (LAN), an intranet, an extranet, or theInternet. The machine may operate in the capacity of a server or aclient machine in a client-server network environment, or as a peermachine in a peer-to-peer (or distributed) network environment. Themachine may be a personal computer (PC), a tablet PC, a networkconnected television, a set-top box (STB), a Personal Digital Assistant(PDA), a cellular telephone, a web appliance, a server, a networkrouter, switch or bridge, or any machine capable of executing a set ofinstructions (sequential or otherwise) that specify actions to be takenby that machine. Further, while only a single machine is illustrated,the term “machine” shall also be taken to include any collection ofmachines that individually or jointly execute a set (or multiple sets)of instructions to perform any one or more of the methodologiesdiscussed herein. In one implementation, computer system 600 may berepresentative of a server, such as server 130, executing musicrecommendation system 140, as described with respect to FIGS. 1-5. Inanother implementation, computer system 600 may be representative of aclient device, such as client device 110, or a content sharing platform,such as content sharing platform 120, as described with respect to FIGS.1-5.

The exemplary computer system 600 includes a processing device 602, amain memory 604 (e.g., read-only memory (ROM), flash memory, dynamicrandom access memory (DRAM) (such as synchronous DRAM (SDRAM) or RambusDRAM (RDRAM), etc.), a static memory 606 (e.g., flash memory, staticrandom access memory (SRAM), etc.), and a data storage device 618, whichcommunicate with each other via a bus 608. Any of the signals providedover various buses described herein may be time multiplexed with othersignals and provided over one or more common buses. Additionally, theinterconnection between circuit components or blocks may be shown asbuses or as single signal lines. Each of the buses may alternatively beone or more single signal lines and each of the single signal lines mayalternatively be buses.

Processing device 602 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 602may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. The processing device 602 is configured to executeinstructions (e.g., processing logic) 626 for performing the operationsand steps discussed herein.

The computer system 600 may further include a network interface device622. The computer system 600 also may include a video display unit 610(e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), analphanumeric input device 612 (e.g., a keyboard), a cursor controldevice 614 (e.g., a mouse), and a signal generation device 620 (e.g., aspeaker).

The data storage device 618 may include a computer-readable storagemedium 624 (also referred to as a machine-readable storage medium), onwhich is stored one or more set of instructions 626 (e.g., software)embodying any one or more of the methodologies of functions describedherein. The instructions 626 may also reside, completely or at leastpartially, within the main memory 604 and/or within the processingdevice 602 during execution thereof by the computer system 600; the mainmemory 604 and the processing device 602 also constitutingmachine-readable storage media. The instructions 626 may further betransmitted or received over a network 674 via the network interfacedevice 622.

The computer-readable storage medium 624 may also be used to storeinstructions to perform a method for providing personalized musicrecommendations, as described herein. While the computer-readablestorage medium 624 is shown in an exemplary implementation to be asingle medium, the term “machine-readable storage medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. A machine-readable mediumincludes any mechanism for storing information in a form (e.g.,software, processing application) readable by a machine (e.g., acomputer). The machine-readable medium may include, but is not limitedto, magnetic storage medium (e.g., floppy diskette); optical storagemedium (e.g., CD-ROM); magneto-optical storage medium; read-only memory(ROM); random-access memory (RAM); erasable programmable memory (e.g.,EPROM and EEPROM); flash memory; or another type of medium suitable forstoring electronic instructions.

The preceding description sets forth numerous specific details such asexamples of specific systems, components, methods, and so forth, inorder to provide a good understanding of several implementations of thepresent disclosure. It will be apparent to one skilled in the art,however, that at least some implementations of the present disclosuremay be practiced without these specific details. In other instances,well-known components or methods are not described in detail or arepresented in simple block diagram format in order to avoid unnecessarilyobscuring the present disclosure. Thus, the specific details set forthare merely exemplary. Particular implementations may vary from theseexemplary details and still be contemplated to be within the scope ofthe present disclosure.

Reference throughout this specification to “one implementation” or “animplementation” means that a particular feature, structure, orcharacteristic described in connection with the implementation isincluded in at least one implementation. Thus, the appearances of thephrase “in one implementation” or “in an implementation” in variousplaces throughout this specification are not necessarily all referringto the same implementation. In addition, the term “or” is intended tomean an inclusive “or” rather than an exclusive “or.”

Although the operations of the methods herein are shown and described ina particular order, the order of the operations of each method may bealtered so that certain operations may be performed in an inverse orderor so that certain operation may be performed, at least in part,concurrently with other operations. In another implementation,instructions or sub-operations of distinct operations may be in anintermittent and/or alternating manner.

What is claimed is:
 1. A method for providing personalized musicrecommendations to a user, the method comprising: identifying, by aprocessing device, a plurality of entities relating to popular searchqueries; selecting, from the plurality of entities, a set of entitiesrepresenting musical artists; determining, based on a history of onlineactions of the user, a subset of the selected set of entities that isrelevant to the user; creating, by the processing device, thepersonalized music recommendations for the user, the personalized musicrecommendations comprising music content associated with the determinedsubset of entities that each represent a musical artist relating to thepopular search queries; and providing the personalized musicrecommendations for presentation to the user.
 2. The method of claim 1wherein the popular search queries are search queries submitted by atleast a threshold number of users via one or more external search engineplatforms over a predefined time period.
 3. The method of claim 1wherein the musical artists comprise one or more of a singer, amusician, a composer, a music video director, a music video producer, ora band.
 4. The method of claim 1 wherein the online actions of the usercomprise one or more of submitting a search query, accessing a mediaitem, or consuming a media item, wherein consuming the media itemcomprises watching or listening to the media item.
 5. The method ofclaim 1 wherein determining, based on the history of online actions ofthe user, the subset of the selected set of entities comprises:identifying musical artists associated with the online actions of theuser; and determining whether any of the identified musical artistmatches any of the selected set of entities relating to the popularsearch queries.
 6. The method of claim 1 wherein creating thepersonalized music recommendations for the user comprises: assigning ascore to each of the determined subset of entities based on popularityof respective search queries and relevancy to the user.
 7. The method ofclaim 6 wherein creating the personalized music recommendations for theuser further comprises: identifying music recommendation candidates thatare ranked based on a plurality of factors; and improving a ranking ofany of the identified music recommendation candidates that matches oneof the determined subset of entities based on a score assigned to arespective entity of the determined subset of entities.
 8. The method ofclaim 1, further comprising: identifying a reference to an artistprofile associated with a musical artist in the determined selectedsubset of entities; and providing the reference for presentation to theuser together with the personalized music recommendations.
 9. The methodof claim 1, further comprising: identifying an online documentindicating why a musical artist in the determined selected subset ofentities is popular; and providing a reference to the online documentfor presentation to the user together with the personalized musicrecommendations.
 10. The method of claim 1, further comprising: creatinga summary indicating why a musical artist in the determined selectedsubset of entities is popular; and providing the summary forpresentation to the user together with the personalized musicrecommendations.
 11. The method of claim 1 wherein the music content ofthe personalized music recommendations is at least one of a playlist ora channel.
 12. A method for providing personalized music recommendationsto a user, the method comprising: identifying, by a processing device ofa content sharing platform, a plurality of music playlists created on acontent sharing platform, each of the plurality of playlists having aranking; identifying a plurality of popular external search queriessubmitted via one or more search engine platforms external to thecontent sharing platform; determining a subset of the plurality of musicplaylists that matches any of the plurality of popular external searchqueries; improving rankings of the determined subset of music playlists;creating, by the processing device, the personalized musicrecommendations for the user based on rankings of the plurality of musicplaylists; and providing the personalized music recommendations forpresentation to the user.
 13. The method of claim 12 wherein theplurality of the popular external search queries are submitted by atleast a threshold number of users over a predefined time period.
 14. Themethod of claim 12 further comprising: determining a plurality ofmusical artists that are each associated with one of the plurality ofmusic playlists; determining, based on a history of online actions ofthe user, a second subset of the plurality of music playlists that isrelevant to the user; and improving rankings of the second subset ofmusic playlists.
 15. The method of claim 14 wherein the plurality ofmusical artists comprises one or more of a singer, a musician, acomposer, a music video director, a lyrics creators, a music videoproducer, or a music band.
 16. The method of claim 14 wherein the onlineactions of the user comprise one or more of submitting a search query,accessing a media item, or consuming a media item, wherein consuming themedia item comprises watching or listening to the media item.
 17. Themethod of claim 12, further comprising: identifying an online documentindicating why a respective external search query is currently popular;and providing a reference to the online document for presentation to theuser together with the personalized music recommendations.
 18. A systemfor providing personalized music recommendations to a user, the systemcomprising: a memory; and a processing device of a content sharingplatform, operatively coupled to the memory, the processing device to:identify a plurality of entities relating to popular search queries;select, from the plurality of entities, a set of entities representingmusical artists; determine, based on a history of online actions of theuser, a subset of the selected set of entities that is relevant to theuser; create the personalized music recommendations for the user, thepersonalized music recommendations comprising music content associatedwith the determined subset of entities that each represent a musicalartist relating to the popular search queries; and provide thepersonalized music recommendations for presentation to the user.
 19. Thesystem of claim 18 wherein to create the personalized musicrecommendations for the user, the processing device is to: assign ascore to each of the determined subset of entities based on popularityof respective search queries and relevancy to the user.
 20. The systemof claim 19 wherein to create the personalized music recommendations forthe user, the processing device is further to: identify musicrecommendation candidates that are ranked based on a plurality offactors; and improve a ranking of any of the identified musicrecommendation candidates that matches one of the determined subset ofentities based on a score assigned to a respective entity of thedetermined subset of entities.
 21. A non-transitory computer-readablestorage medium including instructions that, when accessed by aprocessing device, cause the processing device to: identify a pluralityof music playlists created on a content sharing platform, each of theplurality of playlists having a ranking; identify a plurality of popularexternal search queries submitted via one or more search engineplatforms external to the content sharing platform; determine a subsetof the music playlists that matches any of the plurality of currentlypopular external search queries; improve rankings of the determinedsubset of music playlists; create personalized music recommendations forthe user based on rankings of the plurality of music playlists; andprovide the personalized music recommendations for presentation to theuser.
 22. The non-transitory computer-readable storage medium of claim21 wherein the processing device is further to: determine a plurality ofmusical artists that are each associated with one of the plurality ofmusic playlists; determine, based on a history of online actions of theuser, a second subset of the plurality of music playlists that isrelevant to the user; and improve rankings of the second subset of musicplaylists.